Infrared data-based object evaluation

ABSTRACT

A solution for evaluating an object using infrared data is provided. In particular, infrared data corresponding to the object is processed to enhance a set of signal features. The infrared data is analyzed to determine whether one or more anomalies are present.

REFERENCE TO PRIOR APPLICATIONS

The current application is a continuation of co-pending U.S. patentapplication Ser. No. 13/911,613, entitled “Vehicle Evaluation UsingInfrared Data,” which was filed on 6 Jun. 2013, and which is acontinuation of U.S. patent application Ser. No. 11/748,714, entitled“Vehicle Evaluation Using Infrared Data,” which was filed on 15 May2007, and which claims the benefit of U.S. Provisional Application No.60/854,703, entitled “Multifunctional vehicle inspection system anddevice”, which was filed on 27 Oct. 2006, all of which are herebyincorporated by reference.

GOVERNMENT LICENSE RIGHTS

The U.S. Government has a paid-up license in this invention and theright in limited circumstances to require the patent owner to licenseothers on reasonable terms as provided for by the terms of ContractNumber MC-06-RA-01-G-00000 awarded by the Federal Motor Carrier SafetyAdministration (FMCSA) of the U.S. Department of Transportation.

FIELD OF THE INVENTION

Aspects of the invention relate generally to vehicle evaluation, andmore particularly, to a solution for evaluating a vehicle using infrareddata.

BACKGROUND OF THE INVENTION

Vehicles, particularly commercial vehicles such as trucks, buses, andthe like, transport millions of tons of cargo and millions of passengersto a variety of destinations worldwide. While the overwhelming majorityof these trips are uneventful, a significant number of these tripsexperiences a problem due to a failure of a component of the vehicleitself. Such a problem can cause a delay and/or an accident, the latterof which may result in damage to the vehicle, its cargo, injuredindividual(s), loss of life, and/or the like.

To limit the unanticipated failure of a vehicle component, mostvehicles, and all commercial vehicles, are generally required to undergoregular inspections. Further, additional inspections, particularly ofcommercial vehicles, often are carried out at random times and/orlocations by members of state and/or federal enforcement organizations(e.g., state police, Department of Transportation, etc.). However, witha large number of vehicles involved in a random inspection, it isextremely difficult for an inspector to reliably select the vehiclesmost likely to experience a failure. Often, this is due to the limitedresources and technologies available to the inspector and/or theimplementation of the component. For example, electric and hydraulicbrakes, unlike air brakes, cannot be readily visually evaluated by aninspector since they have no visible moving parts.

It is well known that brakes will heat up when used to slow a vehiclesince the friction will dissipate the motion energy into heat. When abrake is not functioning properly, excessive or insufficient heat may bepresent in the braking area after the brakes have been used. Similarly,other components of a vehicle may show abnormal heat distribution asthey approach failure. For example, improperly functioning bearings mayresult in increased friction, and therefore heat, between a wheel and anaxle. Additionally, a failing tire may have increased heat in an areadue to increased flexing and friction. A heat differential also canindicate other significant phenomena, such as leakage of cargo (e.g.,from a tanker), leakage of exhaust, and/or the like.

Some inspection approaches use heat to determine if a vehicle brakecomponent must be directly tested. For example, an inspector may placehis/her hand near a vehicle's hydraulic or electric brake area todetermine if it appears abnormally warmer than the surrounding air.However, this approach has a number of drawbacks including variations ininspectors and environmental conditions, variations in the amount ofbraking used (e.g., loaded versus unloaded truck), slow and invasiveexamination, which requires the truck to be stopped, and the like.Additionally, another approach uses thermal, or infrared, imaging todetect a defect in a vehicle brake component. In this approach, a humanuser evaluates a thermal image as a vehicle passes an imaging system setup adjacent to a road. However, this approach is limited in that, amongother things, it requires a specially trained individual to evaluate thethermal images and/or operate the system, only a single side of thevehicle is imaged, it fails to address communications with an inspectionsite and/or logging data, and the like.

BRIEF SUMMARY OF THE INVENTION

Aspects of the invention provide a solution for evaluating a vehicleusing infrared data. In particular, evaluation data for the vehicle isobtained, which includes infrared data for a plurality of sides of thevehicle as well as vehicle identification data for distinguishing thevehicle from another vehicle. The infrared data is processed to enhancea set of signal features. Additional non-infrared based data also can beobtained for evaluating the vehicle. The evaluation data is analyzed todetermine whether one or more anomalies are present. The anomaly(ies)can be correlated with a possible problem with a component of thevehicle. Data on the anomaly, problem, and/or vehicle identification canbe provided for use on another system, such as a remote inspectionstation, maintenance system, and/or the like.

A first aspect of the invention provides a method of evaluating avehicle, the method comprising: obtaining evaluation data for thevehicle, the obtaining including: obtaining infrared data for aplurality of sides of the vehicle; processing the infrared data toenhance a signal feature; and obtaining vehicle identification data fordistinguishing the vehicle from another vehicle; analyzing the infrareddata to determine a presence of at least one anomaly; and providing aresult of the analyzing and the vehicle identification data for use at aremote inspection station.

A second aspect of the invention provides a system for evaluating avehicle, the system comprising: a system for obtaining evaluation datafor the vehicle, the system for obtaining including: a system forobtaining infrared data for a plurality of sides of the vehicle; asystem for processing the infrared data to enhance a signal feature; anda system for obtaining vehicle identification data for distinguishingthe vehicle from another vehicle; a system for analyzing the infrareddata to determine a presence of at least one anomaly; and a system forproviding a result of the analyzing and the vehicle identification datafor use at a remote inspection station.

A third aspect of the invention provides a system for evaluating avehicle, the system comprising: a system for automatically detecting thevehicle; a system for obtaining evaluation data for the vehicle, thesystem for obtaining evaluation data including: a first infrared deviceon a first side of the vehicle; a second infrared device on a secondside of the vehicle; and an identification device for obtaining vehicleidentification data for distinguishing the vehicle from another vehicle;and a system for analyzing the evaluation data to determine a presenceof at least one anomaly.

Other aspects of the invention provide methods, systems, programproducts, and methods of using and generating each, which include and/orimplement some or all of the actions described herein. The illustrativeaspects of the invention are designed to solve one or more of theproblems herein described and/or one or more other problems notdiscussed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features of the invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings that depict various embodiments of the invention.

FIG. 1 shows an illustrative environment for evaluating a vehicleaccording to an embodiment.

FIG. 2 shows a more detailed view of the computer system of FIG. 1according to an embodiment.

FIG. 3 shows an illustrative process for evaluating a vehicle accordingto an embodiment.

FIGS. 4A-C show an illustrative acquisition unit according to anembodiment.

FIGS. 5A-C show another illustrative acquisition unit according to anembodiment.

FIG. 6 shows an illustrative configuration of acquisition unitsaccording to an embodiment.

FIG. 7 shows an illustrative vehicle and vehicle wheel, which can beevaluated according to an embodiment.

FIG. 8 shows an illustrative series of images of a wheel and theresulting infrared data after processing with two illustrativeevaluation solutions according to an embodiment.

FIG. 9 shows an illustrative use of a linear array according to anembodiment.

It is noted that the drawings are not to scale. The drawings areintended to depict only typical aspects of the invention, and thereforeshould not be considered as limiting the scope of the invention. In thedrawings, like numbering represents like elements between the drawings.

DETAILED DESCRIPTION OF THE INVENTION

As indicated above, aspects of the invention provide a solution forevaluating a vehicle using infrared data. In particular, evaluation datafor the vehicle is obtained, which includes infrared data for aplurality of sides of the vehicle as well as vehicle identification datafor distinguishing the vehicle from another vehicle. The infrared datais processed to enhance a set of signal features. Additionalnon-infrared based data also can be obtained for evaluating the vehicle.The evaluation data is analyzed to determine whether one or moreanomalies are present. The anomaly(ies) can be correlated with apossible problem with a component of the vehicle. Data on the anomaly,problem, and/or vehicle identification can be provided for use onanother system, such as a remote inspection station, maintenance system,and/or the like. As used herein, unless otherwise noted, the term “set”means one or more (i.e., at least one) and the phrase “any solution”means any now known or later developed solution.

Turning to the drawings, FIG. 1 shows an illustrative environment 10 forinspecting a vehicle 2 according to an embodiment. To this extent,environment 10 includes a computer system 11, which includes variouscomputing devices 12A-B, 14, 16, and 18A-B that can perform the processdescribed herein in order to evaluate vehicle 2. In particular, computersystem 11 includes infrared devices 12A-B, an evaluation device 14, anidentification device 16, and sensing devices 18A-B. During operation,sensing devices 18A-B can detect a presence of a vehicle 2,identification device 16 can obtain identification data for vehicle 2,and each infrared device 12A-B can obtain infrared data from acorresponding side of vehicle 2. Devices 12A-B, 16 can provide the rawdata and/or preprocessed data for further processing on evaluationdevice 14.

Evaluation device 14 can perform advanced image processing on theinfrared data and/or analyze the infrared data to determine whether oneor more anomalies are present. As illustrated, computer system 11 isimplemented in conjunction with an inspection system 40. To this extent,the evaluation of the infrared data can be performed as part of aprescreening process for vehicles 2 being inspected. In this case,evaluation device 14 can communicate the results of the prescreening ofeach vehicle 2 to inspection system 40, which is utilized in performingthe inspection. Inspection system 40 can comprise any type of inspectionsystem now known or later developed. Based on the result for vehicle 2,an inspector can adjust one or more aspects of the inspection (e.g.,perform a more/less thorough inspection of a braking system).

FIG. 2 shows a more detailed view of computer system 11 according to anembodiment. In general, computer system 11 includes various subsystems,which can be implemented on one or more devices. Regardless, evaluationsubsystem 14 (e.g., evaluation device 14 in FIG. 1) can communicate withacquisition subsystem 12 (e.g., one or more of infrared devices 12A-B inFIG. 1), sensing subsystem 18 (e.g., one or more of sensing devices18A-B in FIG. 1), and/or identification subsystem 16 (e.g.,identification device 16 in FIG. 1). In an embodiment, sensing subsystem18 sends a notification to evaluation subsystem 14 when a vehicle 2 isdetected, and evaluation subsystem 14 sends a notification toacquisition subsystem 12 and/or identification subsystem 16 to instructsubsystems 12, 16 to capture data on vehicle 2. Further, sensingsubsystem 18 can send the notification directly to acquisition subsystem12 and/or identification subsystem 16, as illustrated.

In any event, evaluation subsystem 14 is shown implemented as acomputing device 20 that comprises an evaluation program 30, which makescomputing device 20 operable to evaluate vehicle(s) 2 (FIG. 1) byperforming the process described herein. Computing device 20 is shownincluding a processing component 22 (e.g., one or more processors), astorage component 24 (e.g., a storage hierarchy), an input/output (I/O)component 26 (e.g., one or more I/O interfaces and/or devices), and acommunications pathway 28. In general, processing component 22 executesprogram code, such as evaluation program 30, which is at least partiallystored in storage component 24. While executing program code, processingcomponent 22 can read and/or write data to/from storage component 24and/or I/O component 26. Pathway 28 provides a communications linkbetween each of the components in computing device 20, while I/Ocomponent 26 provides a communications link between a user and computingdevice 20. To this extent, I/O component 26 can comprise one or morehuman I/O devices, which enable a human user to interact with computingdevice 20 and/or one or more communications devices to enable a systemuser, e.g., inspection system 40, to communicate with computing device20 using any type of communications link.

Regardless, computing device 20 can comprise any general purposecomputing article of manufacture capable of executing program codeinstalled thereon. However, it is understood that computing device 20and evaluation program 30 are only representative of various possibleequivalent computing devices that may perform the process describedherein. To this extent, in other embodiments, the functionality providedby computing device 20 and evaluation program 30 can be implemented by acomputing article of manufacture that includes any combination ofgeneral and/or specific purpose hardware and/or program code. In eachembodiment, the program code and hardware can be created using standardprogramming and engineering techniques, respectively.

Similarly, computer system 11 is only illustrative of various types ofcomputer systems for implementing aspects of the invention. For example,in one embodiment, evaluation subsystem 14 comprises two or morecomputing devices that communicate over any type of communications link,such as a network, a shared memory, or the like, to perform the processdescribed herein. Further, while performing the process describedherein, one or more computing devices in computer system 11 cancommunicate with one or more other computing devices external tocomputer system 11 using any type of communications link. In any event,a communications link can comprise any combination of various types ofwired and/or wireless links; comprise any combination of one or moretypes of networks; and/or utilize any combination of various types oftransmission techniques and protocols.

As discussed herein, evaluation program 30 enables computing device 20to evaluate a vehicle 2 (FIG. 1). To this extent, evaluation program 30is shown including a vehicle module 32, a processing module 34, ananalysis module 36, and a forwarding module 38. Operation of each ofthese modules is discussed further herein. However, it is understoodthat some of the various modules shown in FIG. 1 can be implementedindependently, combined, and/or stored in memory of one or more separatecomputing devices that are included in computer system 11. Further, itis understood that some of the modules and/or functionality may not beimplemented, or additional modules and/or functionality may be includedas part of computer system 11. Still further, it is understood that thevarious subsystems 12, 14, 16, 18 can be implemented on any combinationof one or more computing devices.

Regardless, aspects of the invention provide a solution for evaluating avehicle, e.g., as part of a pre-inspection for a vehicle inspectionlocation. FIG. 3 shows an illustrative process for evaluating a vehicleaccording to an embodiment, which can be implemented by computer system11 (FIG. 2). Referring to FIGS. 1-3, in process P1, sensing subsystem 18can automatically detect a presence of a vehicle 2. Sensing subsystem 18can comprise any type of system capable of detecting a presence ofvehicle 2 using any solution. For example, sensing subsystem 18 cancomprise an electric eye, which includes a light sensor and/or lightsource (e.g., sensing devices 18A-B), a magnetic sensor, and/or thelike.

In process P2, computer system 11 acquires evaluation data 50 forvehicle 2. Evaluation data 50 can include vehicle identification data 52and infrared data 54. To this extent, identification subsystem 16 canobtain vehicle identification data 52 for distinguishing vehicle 2 fromanother vehicle, and acquisition subsystem 12 can obtain infrared data54 for vehicle 2. In an embodiment, process P2 is performedautomatically in response to a presence of vehicle 2 being detected inprocess P1. To this extent, identification subsystem 16 and acquisitionsubsystem 12 can be located in close proximity to sensing subsystem 18so that a location and/or speed of vehicle 2 is known within a requiredaccuracy. Further, computer system 11 can be located such that onlyvehicles 2 to be inspected are likely to be traveling and be detected.Still further, computer system 11 can be located such that it is highlyprobable that each vehicle 2 has recently applied its brakes, forexample, at a rest area, a weigh station adjacent to a highway, at abottom of a hill, and/or the like.

Identification subsystem 16 can acquire vehicle identification data 52using any solution. To this extent, vehicle identification data 52 cancomprise any type of data for distinguishing vehicle 2 from othervehicle(s) being evaluated. For example, identification subsystem 16 caninclude a radio frequency identification (RFID) tag reader, whichobtains vehicle identification data 52 from an RFID tag on vehicle 2.Further, identification subsystem 16 can include an image/video-basedidentification system, which can obtain at least one visible image ofvehicle 2. Still further, vehicle identification data 52 can includeother data, such as a date/time stamp, a unique identifier (e.g., aserial number, a count), and/or the like. To this extent, vehicle module32 can assign a unique identifier for the data for vehicle 2 upon itsdetection, which is subsequently provided to and used by othersubsystems in computer system 11 to manage the corresponding evaluationdata 50 and track vehicle 2 as it is evaluated and/or inspected.

Acquisition subsystem 12 can obtain infrared data 54 using any solution.For example, acquisition subsystem 12 can comprise a plurality ofinfrared-based imaging devices, which are located on multiple sides ofvehicle 2. Each infrared-based imaging device can acquire a set ofinfrared images for a corresponding side of vehicle 2. In an embodiment,acquisition subsystem 12 includes two infrared devices 12A-B as shown inFIG. 1, each of which can acquire a set of infrared images as vehicle 2passes through the corresponding fields of view. It is understood thatthe number, configuration, and fields of view of infrared devices 12A-Bis only illustrative, and any number of infrared devices 12A-B can beused to obtain infrared image(s) for any side of vehicle 2, includingthe front, back, top, bottom, etc. Additionally, it is understood thatan infrared image may include only a portion of vehicle 2.

Evaluation data 50 can include additional types of data, which can beacquired by acquisition subsystem 12 and/or another subsystem (notshown). For example, evaluation data 50 can include image data based onvisible light, ultraviolet light, and/or the like and/or non-image data,such as radar data, X-ray data, radiation data, magnetic data, pressuredata, spectrometric data, acoustic data, a weight of vehicle 2, and/orthe like. To this extent, acquisition subsystem 12 can obtain anycombination of various types of evaluation data 50 using any solution.For example, acquisition subsystem 12 can include a microphone array toacquire acoustic data for vehicle 2. Similarly, acquisition subsystem 12can include contact-based (e.g., pressure) and/or non-contact-based(e.g., laser/diffuse light) sensor(s) for registering each wheel thatpasses for a vehicle 2. Still further, acquisition subsystem 12 caninclude a set of wireless receivers that can detect signals fromsensor(s) or system(s), such as SAW-based RF tags, implemented onvehicle 2, and which monitor one or more operating characteristics ofvehicle 2 (e.g., tire pressure, engine data, and/or the like).

In process P3, computer system 11 pre-processes evaluation data 50, suchas vehicle identification data 52 and/or infrared data 54. For example,when identification subsystem 16 obtains a set of visible images forvehicle 2, identification subsystem 16 can pre-process the visibleimage(s) to extract, enhance, isolate, and/or the like, vehicleidentification data 52 such as an image of a license plate, operatingcredentials (e.g., located on a side of the vehicle), and/or the like.Further, identification subsystem 16 can obtain vehicle identificationdata 52 that may not uniquely identify vehicle 2. For example,identification subsystem 16 can obtain a color of vehicle 2, asize/shape/type of vehicle 2 (e.g., truck tractor and/or trailer(s)),and/or the like. To this extent, identification subsystem 16 can ensurethat vehicle 2 is a proper type of vehicle that is being inspected(e.g., not a passenger vehicle). Regardless, identification subsystem 16can include the raw data (e.g., image(s)/video of vehicle 2) from whichone or more identifying attributes of vehicle 2 is/are derived asvehicle identification data 52.

Additionally, acquisition subsystem 12 can pre-process some or all ofevaluation data 50, such as infrared data 54. To this extent, eachinfrared device 12A-B can perform filtering, initial processing, and/orthe like, on the infrared data using any solution. For example, infrareddata corresponding to a critical portion of the image can be extracted.Additionally, an infrared image can be filtered to reduce incidentalnoise, glare, and/or the like. Further, an infrared device 12A-B caneliminate shadows, infrared or otherwise, from an image, e.g., usingreflective symmetry detection, common movement, movement conforming toground/field objects, and/or the like. Still further, when evaluationdata 50 includes other types of data, acquisition subsystem 12 canperform noise reduction, signal amplification and smoothing, and/or thelike, on evaluation data 50. The initial processing of evaluation data50 also can include securing the data (e.g., watermarking, encrypting,and/or the like), compressing the data, and/or the like. In any event,acquisition subsystem 12 can store the pre-processed evaluation data 50and/or the raw evaluation data 50 for each vehicle 2.

When computer system 11 includes multiple devices as illustrated in FIG.1, in process P4, the devices that acquire evaluation data 50 cantransmit the data for use on evaluation subsystem 14. For example, eachinfrared device 12A-B and identification device 16 can transmit infrareddata 54 and vehicle identification data 52, respectively, for use onevaluation subsystem 14 using any solution. It is understood that thetransmission can incorporate data security, data compression, and/orother transmission techniques known in the art. In an embodiment, thedata is transmitted using a wireless communications solution. In thismanner, computer system 11 can be readily set up on a temporary orpermanent basis. Regardless, in process P5, evaluation subsystem 14 canreceive evaluation data 50 from subsystems 12, 14 using any solution.Alternatively, one or more subsystems could be implemented on a singledevice, in which case processes P4-P5 may not be required.

In any event, in process P6, processing module 34 can process some orall of evaluation data 50. To this extent, processing module 32 canprocess image-based identification data 52 to extract one or moreidentifying features of vehicle 2 (e.g., a license plate number,operating credentials, and/or the like). Additionally, vehicle module 32can match identification data, such as RFID tag information, a licenseplate number, operating credentials, and/or the like, with a knowndatabase of vehicles 2, e.g., in a state/national database, privatefleet, and/or the like. Further, processing module 34 can performadvanced image processing on infrared data 54 to enhance (e.g., define,extract, identify, and/or the like) one or more signal features. Forexample, processing module 34 can subject infrared data 54 to one ormore of segmentation, fusion between multiple images, feature detection,and/or the like. Additionally, when evaluation data 50 includes othertypes of data, processing module 34 can fuse different types of data inevaluation data 50. By using data fusion, processing module 34 canprovide redundant evaluation data 50 that is more susceptible foraccurate detection of various phenomena, and is less prone to falsepositive/negative readings.

In process P7, analysis module 36 can analyze evaluation data 50 todetermine whether any anomalies may be present. To this extent, analysismodule 36 can implement any combination of various decision-makingsolutions including a trained neural network, an expert system, templatematching, a Markov Model, and/or the like. These decision-makingsolutions can examine some or all of evaluation data 50 to determinewhether an anomaly is present. For example, a set of infrared images canbe examined to determine whether one or more areas may exhibit heat thatis outside of an expected range, e.g., either too cold or too hot.Analysis module 36 can analyze evaluation data 50 for the presence ofvarious types of anomalies. Different types of evaluation data 50 may bemore useful for determining different types of anomalies. For example,visible image data could be used to determine whether a leak, a loosehose, or the like, may be present on the vehicle, while ultravioletimage data could be used to identify the presence of excess strain orthe like. Further, analysis module 36 can obtain anomaly informationfrom one or more external sources. For example, analysis module 36 canprovide a license plate, operating credentials, and/or the like, forcomparison with a law enforcement database, maintenance historydatabase, and/or the like. In this case, analysis module 36 can receivea response that indicates whether an anomaly may be present due tooperation of the vehicle itself (e.g., stolen vehicle, suspendedlicense, past due maintenance, and/or the like).

In decision D1, analysis module 36 can determine whether one or moreanomalies are present. If so, in process P8, analysis module 36 canstore anomaly data 56 for the vehicle. Anomaly data 56 can includeinformation on the anomaly(ies) located in evaluation data 50. Further,anomaly data 56 can include one or more recommended actions as a resultof the anomaly(ies). For example, anomaly data 56 can include arecommended type of inspection and/or area of inspection. When computersystem 11 is implemented as a preliminary evaluation system (e.g., apre-inspection system), in process P9, forwarding module 38 can transmitsome or all of evaluation data 50, including anomaly data 56, for use bya primary evaluation system, such as inspection system 40, for temporaryor permanent storage, and/or the like.

It is understood that the process is only illustrative of variousprocesses that can be implemented. For example, forwarding module 38 canprovide a result of the analysis together with vehicle identificationdata 52 for use by a remote, primary evaluation system, such asinspection system 40, for every vehicle, regardless of whether anyanomaly(ies) were detected. Further, vehicle module 32 can manage allevaluation data 50 for one or more vehicles using any solution. To thisextent, vehicle module 32 can store evaluation data 50 using anysolution (e.g., one or more files, records in a database, and/or thelike). Further, vehicle module 32 can manage an interface such as a userinterface, application program interface (API), and/or the like, whichenables a user to perform one or more operations on evaluation data 50.Still further, vehicle module 32 can automatically perform maintenance,such as purging evaluation data 50 that is no longer required, using anysolution.

As described herein, an embodiment of acquisition subsystem 12 utilizesacquisition devices, such as infrared devices 12A-B, which are portableand can be readily deployed and/or removed. To this extent, FIGS. 4A-Cshow an illustrative acquisition unit 60 according to an embodiment. Asillustrated in FIGS. 4A-B, acquisition unit 60 includes a power unit,which can include solar panels 62A-B, a support structure, which caninclude foldable legs 64A-C, and a sensor head 66. It is understood thatsolar panels 62A-B are only an illustrative solution for generatingpower for acquisition unit 60, and other solutions, including a powerunit that does not include independent power generation (e.g., only abattery), can be utilized. Similarly, it is understood that foldablelegs 64A-C are only illustrative, and the support structure can beimplemented using any solution.

FIG. 4C shows a more detailed view of sensor head 66 according to anembodiment. Sensor head 66 includes an acquisition bay 70, anelectronics bay 72, and an interface bay 74. Each bay 70, 72, 74 caninclude one or more components that implement various functions. Forexample, acquisition bay 70 can include a set of data acquisitiondevices, such as an infrared device 70A. Electronics bay 72 can includea set of components for data processing and storage, wirelesscommunications, power supply and distribution components, and/or thelike. Additionally, interface bay 74 can include one or more I/Ointerface ports (e.g., Ethernet, USB, Firewire, and/or the like), one ormore I/O interface devices (e.g., display, keypad, and/or the like), apower interface (e.g., for a rechargeable battery), and/or the like.Further, sensor head 66 can include an antenna 76 for sending and/orreceiving data via a wireless communications system, and a mountingcollar 78 for permanently or temporarily mounting sensor head 66 to apermanent or portable support structure.

Acquisition bay 70 can include any type of data acquisition device(s)for acquiring a particular type of evaluation data 50 (FIG. 2). To thisextent, infrared device 70A can comprise any type of infrared imagingdevice. For example, infrared device 70A can detect infrared radiationusing an un-cooled microbolometer, an un-cooled line-scan camera,lower-resolution infrared imaging system, and/or the like. These typesof infrared devices require less power than devices that utilize acooled infrared sensor, which also can be implemented when sufficientpower is not an issue (e.g., a permanent emplacement). When a line-scancamera is utilized, processing module 34 (FIG. 2) can combine thescanned infrared data with information on the vehicle's speed to producean accurate scaled image from the successive slices scanned as thevehicle passed. Additionally, infrared device 70A can comprise: anear-infrared (NIR) imaging device, which is best when an anomaly isindicated by a several hundred degrees temperature difference; amedium-wave infrared (MWIR) imaging device, which can penetrate fog andvery humid air; or a long-wave infrared (LWIR) imaging device. Moreover,infrared device 70A can comprise an imaging device that combines alower-resolution infrared image with a higher-resolution visible lightimage to provide a fused infrared and visible light-based image.

Further, infrared device 70A could comprise a fixed imaging unit or ascanning sensor unit, such as a pan-tilt-zoom imaging device. In thelatter case, infrared device 70A can be controlled by acquisitionsubsystem 12 (FIG. 2) and can scan key locations on vehicle 2 (FIG. 1).As a result, infrared device 70A can acquire zoomed, higher resolutionimages of the key locations for subsequent analysis by the remainder ofcomputer system 11 (FIG. 2). Additionally, with a zoom capability,infrared device 70A can be placed at a greater distance from vehicle 2and still obtain high resolution image data for vehicle 2.

An image acquired by infrared device 70A can be blurred when the imageis captured before the microbolometers deplete their existing chargefrom acquiring a previous image. Infrared device 70A and/or electronicsbay 72 can include one or more components to address this problem usingany solution. For example, infrared device 70A can include an externalphysical shutter that shuts down the frame for a sufficient period oftime to clear the infrared device 70A. Further, infrared device 70A canincorporate a wide field of view to avoid a large shift in the imagefrom frame to frame. Still further, electronics bay 72 can process theimage(s) using an image deblurring algorithm, or the like. In this case,information on the speed of vehicle 2 (FIG. 1) may be obtained (e.g.,using a radar, visible-light movement analysis, and/or the like) andutilized by the algorithm. Still further, infrared device 70A canincorporate a lower time constant microbolometer, a cooled imager,and/or the like.

Infrared device 70A and/or electronics bay 72 also can incorporate oneor more features for helping to ensure accurate infrared images. Forexample, infrared device 70A and/or electronics bay 72 can compensatefor infrared drift offset. In particular, during use, the response toinfrared radiation of pixels in infrared device 70A can shift slightlyin unpredictable patterns. As a result, infrared device 70A can includea shutter-based recalibration, in which a shutter closes off the cameraand the pixel offsets can be recalibrated to a temperature of theshutter. Frequently, the shutter-based recalibration is triggeredperiodically. In an embodiment, infrared device 70A and/or electronicsbay 72 can trigger recalibration to occur frequently when a vehicle isnot present so that infrared device 70A will have been recentlyrecalibrated when a new vehicle is present. Additionally, infrareddevice 70A can include a “cold shield” that surrounds the infraredsensors and prevents any infrared interference resulting from heatradiating from one or more components in acquisition unit 60.

Sensor head 66 can be designed to operate in various weather conditionsand withstand frequent movement. In particular, sensor head 66 caninclude a rugged construction, and include various solutions forprotecting the operating components from rain, snow, and/or the like.Moreover, sensor head 66 can include various ancillary systems to ensureproper operation of the various components therein. To this extent,sensor head 66 can include environmental/system monitoring components,self-cleaning components, and/or the like.

Regardless, it is understood that sensor head 66 is only illustrative.For example, sensor head 66 could be implemented as a handheld device,which can be pointed at a vehicle 2 (FIG. 1) and acquire and process therelevant evaluation data 50 (FIG. 2). Further, in another embodiment,sensor head 66 and/or acquisition unit 60 includes some or all of thecomponents for sensing subsystem 18 (FIG. 2) and/or identificationsubsystem 16 (FIG. 2). To this extent, sensing subsystem 18 couldcomprise a “radar gun”, which can detect vehicle 2 at a sufficient rangeto prepare the other components for operation. Additionally,identification subsystem 16 could comprise a visible imaging device orthe like, and the image(s) can be utilized by both identificationsubsystem 18 and acquisition subsystem 12.

Returning to FIGS. 1 and 2, acquisition subsystem 12 may include two ormore acquisition units 60 (FIG. 4A) that are located on a plurality ofsides of vehicle 2. For example, each infrared device 12A-B can belocated on a corresponding acquisition unit 60. Similarly, sensingsubsystem 18 and/or identification subsystem 16 can include one or moreunits that are configured similarly to acquisition unit 60, but includethe appropriate sensing devices and processing capabilities to implementthe corresponding functions for the respective subsystems 16, 18.

However, this is only illustrative, and acquisition subsystem 12,identification subsystem 16, and/or sensing subsystem 18 can include anacquisition unit in an alternative location. For example, FIGS. 5A-Cshow another illustrative acquisition unit 80 according to anembodiment. In particular, as shown in FIG. 5A, acquisition unit 80 islocated in a path of vehicle 2 such that vehicle 2 will pass overacquisition unit 80. In this case, acquisition unit 80 can acquireevaluation data 50 (FIG. 2) from an interior side of vehicle 2. To thisextent, acquisition unit 80 can acquire evaluation data 50 for aninterior side of one or more wheels 4 (as shown). Further, acquisitionunit 80 can acquire evaluation data 50 for an undercarriage of vehicle2. Still further, as vehicle 2 is approaching and/or leaving thelocation of acquisition unit 80, acquisition unit 80 can acquireevaluation data 50 for a front and/or back of vehicle 2. Acquisitionunit 80 can be permanently or temporarily placed at a location using anysolution.

Acquisition unit 80 can include similar components as shown anddescribed with respect to sensor head 66 (FIG. 4C). To this extent,acquisition unit 80 can include an imaging device, such as an infrareddevice and/or visible imaging device, for each side of vehicle 2 to beimaged, data processing, storage, interface, and communicationscomponents, a power source, and/or the like. In an embodiment,acquisition unit 80 includes a single imaging device for a plurality ofsides of vehicle 2 to be imaged. For example, as shown in FIGS. 5B-C,acquisition unit 80 can include a single imaging device 82 (e.g., aninfrared device, shown only in FIG. 5B for clarity) that images light(e.g., infrared light) that passes through both windows/lenses 84A-B.The light is then reflected from mirrors 86A-B onto a concave mirror 88and then a convex mirror 90, which directs the light through atransparent portion 88A of concave mirror 88 and onto an imaging sensorof imaging device 82.

Acquisition unit 80 can include electronic shutters 92A-B (shown only inFIG. 5B for clarity) that alternatively block light passing through oneof windows/lenses 84A-B to enable a clear acquisition of both fields ofview. Electronic shutters 92A-B can operate at a speed commensurate witha frame rate of imaging device 82, thereby enabling each field of viewto be imaged every nth frame (where n is the number of fields of view,two in this embodiment). Alternatively, imaging device 82 could imageeach field of view in a unique subdivision of its imaging area, therebyenabling simultaneous imaging of all fields of view.

FIG. 6 shows an illustrative configuration of acquisition units 94A-Caccording to an embodiment. In this case, acquisition units 94A-C areconfigured for use in a high speed environment (e.g., a highway). Asillustrated, acquisition units 94A-C are mounted to supports for roadsignage or the like. This can enable acquisition units 94A-C to acquireinfrared data for vehicles 2 from various angles. Further, road 96and/or other surfaces can include reflective material that can directinfrared data towards one or more acquisition units, such as acquisitionunit 94A. Similarly, acquisition units 94A-C could be placed at other,lower speed locations, such as toll booths, or the like.

Returning to FIG. 2, depending on the type(s) of evaluation data 50acquired and processed, analysis module 36 can detect any combination ofvarious types of anomalies, and the corresponding flaws, that may bepresent on a vehicle 2 (FIG. 1). For example, analysis module 36 candetect acoustic anomalies, which can be used to identify a flawed (e.g.,worn) bearing, malfunctioning engine, and/or the like. Further, someevaluation data 50 may identify an anomaly directly, such as asufficiently high measurement of radiation data.

As discussed herein, analysis module 36 can use infrared data 54, suchas one or more infrared images of vehicle 2, alone and/or in conjunctionwith other types of data to determine the presence of any anomalies invehicle 2. To this extent, infrared data 54 that includes one or moreareas that are hotter than normal can be used to detect defects such as:stuck brake, under-inflated/flat tire, leaking exhaust (heat in abnormalarea), worn bearings, overheating engine compartment, cracked frame(causing additional flexing and therefore additional heat), overheatingradiator, overheating cargo (e.g., chemicals or biologicals which mayreact with each other to create additional heat or even catch onfire—compost, rags filled with oil and other reactive solvents, sodium,etc.), and/or the like. Similarly, infrared data 54 that includes one ormore areas that are cooler than normal can detect defects such as:failing brakes, non-operating brakes, loss of cooling in refrigeratedarea, and/or the like. Still further, analysis module 36 can processinfrared data 54 to measure one or more attributes of a portion ofvehicle 2, which analysis module 36 then uses to determine the presenceof a defect. For example, analysis module 36 can thermally map treaddepth for a wheel 4 (FIG. 5A) of vehicle 2 using infrared data 54, whichanalysis module 36 can compare to a standard to determine whether thetread depth is sufficient. Further, analysis module 36 can use infrareddata 54 to identify unexpected voids within a cargo area (which wouldaffect heat transmission through vehicle 2), which can then be flaggedfor a follow up inspection.

Additional illustrative details are described with reference to the useof infrared image(s) to detect one or more anomalies with respect to thebrakes, bearings, and/or wheels of a vehicle. To this extent, FIG. 7shows an illustrative vehicle 2 and vehicle wheel 4, which can beevaluated according to an embodiment. In general, wheel 4 includes awheel rim 6 and a tire 8. Wheel rim 6 is attached to a central axle 6Aand includes a bearing area 6B (the bearings are internal in this area)and a plurality of holes, such as hole 6C, through which a brake drumcan be viewed. Tire 8 is affixed to wheel rim 6 and contacts the roadalong a tread surface 8A.

When the brakes of vehicle 2 are applied, friction occurs at the brakedrum and the corresponding heat dissipates through holes 6C. Similarly,a worn bearing will cause additional heat in bearing area 6B than thatseen for a properly operating bearing. Further, abnormal heating oftread surface 8A will occur due to under-inflation, tread separation,and/or the like. In each case, analysis module 36 (FIG. 2) can examineinfrared data 54 (FIG. 2) that includes a set of infrared images, eachof which includes some or all of wheel 2 to determine the presence ofone or more anomalies. In particular, analysis module 36 can examine therelevant portion(s) of wheel rim 6 and tread surface 8A to determinewhether any brake, wheel, and/or bearing-related anomaly(ies) is/arepresent on vehicle 2.

Analysis module 36 (FIG. 2) can process infrared image(s) of wheel 4using any combination of one or more image processing algorithms. Forexample, FIG. 8 shows an illustrative series of images 100A-C of a wheeland the resulting infrared data after processing with two illustrativeevaluation solutions according to an embodiment. In particular, analysismodule 36 can generate infrared data 102A-C by processing thecorresponding images 100A-C using an edge detection algorithm. The edgedetection algorithm detects edges in an image by analyzing localbrightness changes over short distances. Similarly, analysis module 36can generate infrared data 104A-C by processing the corresponding images100A-C using a thresholding algorithm. The thresholding algorithmassigns each pixel to white or black, depending on whether a brightnesslevel of the pixel exceeds a threshold brightness level.

Image 100A corresponds to a wheel 4 (FIG. 7) having cold brakes, image100B corresponds to a wheel 4 having warm brakes, and image 100Ccorresponds to a wheel 4 having hot bearings. As can be seen, theinfrared data 102A-C generated by applying an edge detection algorithmyields a clear distinction between cold brakes infrared data 102A andwarm brakes infrared data 102B. However, only a minimal difference ispresent between warm bearings infrared data 102C and the normal bearingsof infrared data 102A-B. As a result, the edge detection algorithm maynot efficiently detect warm bearings. Additionally, the infrared data104A-C generated by applying a thresholding algorithm can be used toreadily distinguish between both cold brakes (infrared data 104A) andwarm brakes (infrared data 104B) and hot bearings (infrared data 104C)and normal bearings (infrared data 104A-B).

It is understood that each algorithm can be calibrated to successfullyevaluate infrared images 100A-C. For example, the thresholding algorithmcan be calibrated so that the threshold brightness level is set to anexpected brightness, which is based on a level of brightness thatcorresponds to proper braking and/or bearing operation. The thresholdingalgorithm can be executed multiple times, each with an expectedbrightness that corresponds to an anomaly. For example, a first expectedbrightness may be set to a highest acceptable brightness, above whichthe brake is labeled as “hot”; a second expected brightness may be setto a lowest acceptable brightness, below which the brake is labeled as“cold”; and a third expected brightness may be set to a highestacceptable brightness, above which the bearings are labeled as “hot”.After each application of the thresholding algorithm, the resultinginfrared data can be analyzed using any solution. The expectedbrightness may be adjusted based on one or more factors, such as ambientconditions, a weight of the vehicle, a typical amount of recent braking,and/or the like.

Returning to FIG. 2, it is understood that the edge detection andthresholding algorithms are only illustrative of various types and/orcombinations of algorithms that analysis module 36 can implement. Forexample, with proper calibration, analysis module 36 can perform athermal mapping of infrared images 100A-C (FIG. 8) and compare theactual heat values with expected values across different areas of awheel 4 (FIG. 7) and/or vehicle 2 (FIG. 7). Further, analysis module 36can process an infrared image of tread surface 8A (FIG. 7) to measure adepth of the tread, in which a temperature difference between high andlow points depends on a thickness of the tread (e.g., a thicker treadwill have a greater difference than a thinner tread). Still further,analysis module 36 can implement a curve-fitting algorithm to match edgefeatures detected by the edge detection algorithm to a circle matchingan expected area for detecting a brake cylinder. Still further, analysismodule 36 can implement algorithms such as contrast enhancement, imagehistogram adjustment, blob and object detection, and/or the like.Additionally, evaluation data 50 can include other types of data forwhich analysis module 36 can implement similar algorithms for detectingrelevant signal features in the data and discriminating between theabsence or presence of one or more anomalies.

In an embodiment, acquisition subsystem 12 detects a set of infraredsignatures for vehicle 2 (FIG. 1). For example, acquisition subsystem 12can include a linear array of infrared sensing devices that captureinfrared data. FIG. 9 shows an illustrative use of a linear array 110according to an embodiment. In general, vehicle 2 can pass linear array110, which includes a plurality of infrared-sensing elements at varyingheights. The data obtained by the infrared-sensing elements can be usedto produce a set of infrared signatures 112 for vehicle 2 (e.g., byaveraging or otherwise combining all the data, generating infraredsignatures for multiple heights—wheel centerline, undercarriage height,etc.). The infrared signature(s) 112 can be normalized against anambient temperature plot 114 can compared to an expected signature 116.A significant variation (e.g., based on a max/min temperature ininfrared signature 112, a goodness of fit to expected signature 116,and/or the like) of infrared signature 112 from the expected signature116 can identify a potential anomaly and/or be flagged for furtherinspection. For example, the variation shown in FIG. 9 may indicate thatmiddle wheel of vehicle 2 is overheating. Further, it is understood thatlinear array 110 can be used to acquire image(s) of vehicle 2 by, forexample, passively scanning vehicle 2 (e.g., imaging vehicle 2 indiscrete sections as it passes), actively scanning vehicle 2 (e.g.,using a mechanism to cause a field of view of linear array 110 to sweepa length of vehicle 2), and/or the like. These images can be used in thesame manner as discussed herein.

The management (e.g., selection, calibration, utilization, etc.) of aset of algorithms for evaluating a vehicle 2 (FIG. 7) can be implementedusing any type of artificial intelligence solution. For example,analysis module 36 can implement an expert system that comprises a setof condition examinations and corresponding action events, to evaluatevehicle 2. Such an expert system could include a condition examinationsuch as a number of white pixels in infrared data 104A-C (FIG. 8), andperform some action (e.g., further analyze evaluation data 50 forhot/cold brakes, worn bearings, and/or the like) if the number isbelow/above an expected range. It is understood that the expert systemcan implement fuzzy logic (e.g., assign probabilities) to arrive at aconclusion with respect to the presence/absence of an anomaly in vehicle2. Further, analysis module 36 can implement a neural network, whichincludes a series of connected neural units, each of which triggers upona certain set of conditions. The neural network can be trained usingsample data and include an ability to self-modify the characteristics ofthe units in the neural network using backpropagation or the like. Stillfurther, analysis module 36 can implement template or pattern matching,in which evaluation data 50 is compared to a set of templates orpatterns that have particular characteristics of interest. In this case,a proper tolerance for assigning matches is critical so that evaluationdata 50 is not under or over matched with the corresponding templates orpatterns.

As discussed herein, evaluation data 50 can include multiple types ofdata on vehicle 2 (FIG. 1), which can be obtained at varying locationsand/or times, and which have been fused by processing module 34. In thiscase, analysis module 36 can compare features detected in the differenttypes of data, and eliminate some potential confounding variables thatcan yield a false positive/negative. For example, holes 6C (FIG. 7) maybe visible in a visible image and only visible in infrared data if ananomaly is present. When visible in the infrared data, analysis module36 can overlay the locations with the visible image to verify a locationof the hot spots. Additionally, acoustic data can be used to determinewhether a sound typically associated with a failing bearing is presentwhen a bearing-related anomaly is indicated by infrared data. Stillfurther, visible light and infrared-based profiles of vehicle 2 can becompared to determine the location of infrared heat signatures ascompared with known vehicular systems.

To assist in calibrating computer system 11, an infrared calibrationfixture can be obtained, which includes a thermal grid pattern tailoredfor use in calibrating computer system 11 using any solution. Aninfrared device, such as infrared devices 12A-B (FIG. 1), can include anautomatic internal shutter that provides relative zeroing capability.For example, the internal shutter can provide an imager with aneffectively even temperature at all points for calibration betweenpixels. To this extent, infrared arrays can include pixels of differentspecific reactivity, sensitivity, and gain, which must be calibrated toproduce an accurate and even image.

Additionally, it may be desirable to determine an approximatetemperature of objects/features detected in an infrared image. To thisextent, a target (such as the infrared calibration fixture) having areasof known temperatures (within a tolerance) can be placed in the field ofview of an infrared device 12A-B (FIG. 1) during imaging of vehicle 2(FIG. 1). In this case, the imaged target can be compared with theinfrared image of vehicle 2 to determine a temperature corresponding tothe objects/features detected on vehicle 2. The target can beconstructed using heating/cooling elements and thermostatic elementsthat ensure that the temperature of the components is maintained towithin relatively small tolerances. Additionally, the target can becustomized to fit appropriate infrared emissivity profiles of one ormore components, e.g., of braking components, thereby providing similarpatterns that would be seen from the corresponding component(s).

Further, the fields of view for two or more infrared devices 12A-B canbe registered with one another so that the locations of common elementscan be accurately determined. A band-pass filter or the like can be usedto adjust a temperature sensing range for of one or more of the infrareddevices 12A-B to a slightly different band. Differences in the imagescaptured by the infrared devices 12A-B would then be due to differencesin the heat radiation emitted. These differences can be used to generatea temperature map for vehicle 2. Alternatively, a single infrared device12A-B with switchable and/or tunable filters may be used.

An inherent temperature sensing range for many infrared devices 12A-Bmay be narrower than that of a range of temperatures of potentialinterest. For example, many commercial infrared devices 12A-B “top out”at around 500 degrees Fahrenheit, while exhaust gases, highly heatedcomponents of exhaust systems, failing brakes/bearing, and/or the like,may have temperatures around 1,000 degrees Fahrenheit. To this extent,acquisition subsystem 12 can include one or more components to detecttemperature variations across wider ranges of temperatures. In anembodiment, acquisition subsystem 12 uses a neutral-density filter toeffectively reduce the radiation by a factor (e.g., 3:1, 10:1, and/orthe like) across the sensitive band of infrared devices 12A-B. In thiscase, saturation will not occur until a much higher temperature, butsome sensitivity will be lost. For example, using a factor of 3:1 willresult in a 1500 degree span being imaged across a 500 degree span.

Additionally, acquisition subsystem 12 can fuse multiple captured frameshaving different characteristics (e.g., longer exposure, differentfilters, and/or the like) into a higher-bit resolution digital imagethat maintains the temperature information in each separate frame. Forexample, three frames, each of which has a 300 degree sensitive range inthree adjacent ranges (e.g., 0-300, 300-600, 600-900) can be utilized.The frames can be superimposed with sufficient color and brightness bitresolution to discriminate clearly between all three conditionsthroughout the fused frame. When performed on a moving target, such asvehicle 2 (FIG. 1), acquisition subsystem 12 must register the imagesacross the frames, compensate for the movement, potential blurring,and/or the like, and combine the images into a single registeredcomposite image for analysis.

Computer system 11 is described herein as performing a preliminaryevaluation of vehicles, and providing some or all of evaluation data 50for use by an inspection system 40. A particular embodiment wouldinclude implementation of computer system 11 as part of a commercialvehicle inspection station (e.g., weigh station). In this manner,individuals using the inspection system 40 can use the data to perform amore focused inspection of a particular vehicle and/or set of vehicles,while allowing vehicles without any detected anomalies to proceed morequickly through the inspection. As a result, inspection system 40 canmore efficiently inspect vehicles while removing a higher percentage ofunsafe vehicles from operation.

However, it is understood that this embodiment is only illustrative. Forexample, computer system 11 can be implemented as part of a fleetmanagement system for a fleet of vehicles, such as commercial vehiclesor buses. In this case, computer system 11 can obtain a historicalrecord of previous inspection(s) and can permit condition-based ratherthan schedule-based maintenance on the vehicles. As a result, a fleetowner will only need to replace parts that are actually out of a giventolerance range, saving the expense of replacing parts too early. Suchan embodiment can evaluate vehicles as they arrive and/or depart to/froma destination (e.g., a warehouse). Additionally, a third partymaintenance company could charge the fleet owner only for thosevehicles/components that are found out of tolerance. Further, computersystem 11 can be integrated into other types of operations, such assecurity applications, manufacturer databases, governmental regulationcompliance systems, and/or the like.

Further, while aspects of the invention have been shown and describedwith respect to the use of infrared data with or without other types ofdata in evaluating a vehicle, it is understood that alternativeembodiments may be implemented without the use of infrared data. To thisextent, embodiments may obtain evaluation data that includes image databased on visible light, ultraviolet light, and/or the like and/ornon-image data, such as radar data, X-ray data, radiation data, magneticdata, pressure data, spectrometric data, acoustic data, a weight, and/orthe like. The particular combination of types of data can be variedbased on a particular application of the embodiment. For example, anembodiment can obtain ultraviolet light-based image data that is used toevaluate a presence of an unacceptable amount of strain for one or moreparts of the vehicle. Additionally, an embodiment can obtain acousticdata, which can be evaluated to determine engine performance, bearingperformance, and/or the like.

While shown and described herein as a method and system for evaluating avehicle, it is understood that the invention further provides variousalternative embodiments. For example, in one embodiment, the inventionprovides a computer program stored on a computer-readable medium, whichwhen executed, enables a computer system to evaluate a vehicle. To thisextent, the computer-readable medium includes program code, such asevaluation program 30 (FIG. 2), which implements the process describedherein. It is understood that the term “computer-readable medium”comprises one or more of any type of tangible medium of expressioncapable of embodying a copy of the program code (e.g., a physicalembodiment). In particular, the computer-readable medium can compriseprogram code embodied on one or more portable storage articles ofmanufacture, on one or more data storage portions of a computing device,such as storage component 24 (FIG. 2), as a data signal traveling over anetwork (e.g., during a wired/wireless electronic distribution of thecomputer program), on paper (e.g., capable of being scanned andconverted to electronic data), and/or the like.

In another embodiment, the invention provides a method of generating asystem for evaluating a vehicle. In this case, a computer system, suchas computer system 11 (FIG. 1), can be obtained (e.g., created,maintained, having made available to, etc.) and one or moreprograms/systems for performing the process described herein can beobtained (e.g., created, purchased, used, modified, etc.) and deployedto the computer system. To this extent, the deployment can comprise oneor more of: (1) installing program code on a computing device, such ascomputing device 20 (FIG. 2), from a computer-readable medium; (2)adding one or more computing devices to the computer system; and (3)incorporating and/or modifying one or more existing devices of thecomputer system, to enable the computer system to perform the processdescribed herein.

In still another embodiment, the invention provides a business methodthat performs the process described herein on a subscription,advertising, and/or fee basis. That is, a service provider could offerto evaluate one or more vehicles as described herein. In this case, theservice provider can manage (e.g., create, maintain, support, etc.) acomputer system, such as computer system 11 (FIG. 1), that performs theprocess described herein for one or more customers. In return, theservice provider can receive payment from the customer(s) under asubscription and/or fee agreement, receive payment from the sale ofadvertising to one or more third parties, and/or the like.

As used herein, it is understood that “program code” means any set ofstatements or instructions, in any language, code or notation, thatcause a computing device having an information processing capability toperform a particular function either directly or after any combinationof the following: (a) conversion to another language, code or notation;(b) reproduction in a different material form; and/or (c) decompression.To this extent, program code can be embodied as any combination of oneor more types of computer programs, such as an application/softwareprogram, component software/a library of functions, an operating system,a basic I/O system/driver for a particular computing, storage and/or I/Odevice, and the like.

The foregoing description of various aspects of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed, and obviously, many modifications and variations arepossible. Such modifications and variations that may be apparent to anindividual in the art are included within the scope of the invention asdefined by the accompanying claims.

What is claimed is:
 1. A method of evaluating an object for a heatrelated anomaly, the method comprising: processing, by a computer systemincluding at least one computing device, infrared data corresponding toat least one side of the object to enhance a set of signal features inthe infrared data and extract infrared data corresponding to at least aportion of the object from the infrared data, wherein the processinguses at least one enhancement process selected from a group ofenhancement processes based on the at least one heat related anomaly,the group of enhancement processes including: segmentation, fusion ofmultiple infrared images in the infrared data, edge detection within aninfrared image in the infrared data, applying thresholding to assign apixel in the infrared data to white or black, and feature detection ofthe at least a portion of the object in the infrared data; analyzing theinfrared data to determine a presence of the at least one heat relatedanomaly; and providing a result of the analyzing for use in anevaluation of the object.
 2. The method of claim 1, further comprisingobtaining evaluation data, wherein the evaluation data includes theinfrared data and at least one type of non-infrared based datacorresponding to the object, wherein at least one of the processing oranalyzing further uses the at least one type of non-infrared based data.3. The method of claim 1, further comprising obtaining evaluation data,the obtaining evaluation data including: acquiring a first infraredimage for a first side of the object using a first infrared devicehaving a first field of view; and acquiring a second infrared image fora second side of the object using a second infrared device having asecond field of view, wherein the first and second fields of view areconfigured to facilitate identifying a location of an element visible inboth the first and second infrared images.
 4. The method of claim 1, theanalyzing including examining a portion of each of a plurality ofinfrared images that includes a component of the object subject toheating during use of the component.
 5. The method of claim 4, theexamining including comparing a brightness of a portion of the componentwith an expected brightness.
 6. The method of claim 1, furthercomprising obtaining the infrared data using a set of infrared devices,wherein the object is a component of a vehicle.
 7. The method of claim6, wherein at least one of the set of infrared devices is located off ofthe vehicle.
 8. The method of claim 1, further comprising pre-processingthe infrared data prior to the processing, wherein the pre-processingincludes at least one of: applying an image deblurring algorithm to atleast a portion of the infrared data or filtering at least a portion ofthe infrared data.
 9. A system comprising: a computer system configuredto evaluate an object for at least one heat related anomaly, thecomputer system including at least one computing device for performing amethod comprising: processing infrared data corresponding to at leastone side of the object to enhance a set of signal features in theinfrared data and extract infrared data corresponding to at least aportion of the object from the infrared data, wherein the processinguses at least one enhancement process selected from a group ofenhancement processes based on the at least one heat related anomaly,the group of enhancement processes including: segmentation, fusion ofmultiple infrared images in the infrared data, edge detection within aninfrared image in the infrared data, applying thresholding to assign apixel in the infrared data to white or black, and feature detection ofthe at least a portion of the object in the infrared data; analyzing theinfrared data to determine a presence of the at least one heat relatedanomaly; and providing a result of the analyzing for use in anevaluation of the object.
 10. The system of claim 9, further comprisinga set of infrared devices configured to acquire the infrared data. 11.The system of claim 10, wherein the infrared data is acquired during asingle pass of the object, the method further comprising: automaticallydetecting a presence of the object; and automatically obtainingevaluation data in response to the detected presence, wherein theevaluation data includes the infrared data.
 12. The system of claim 10,further comprising an imaging device configured to acquire visible imagedata of the object, wherein at least one of the processing or analyzingfurther uses the visible image data.
 13. The system of claim 10, whereinthe set of infrared devices includes: a first infrared device configuredto acquire a first infrared image having a first field of view for afirst side of the object; and a second infrared device configured toacquire a second infrared image having a second field of view for asecond side of the object, wherein the first and second fields of vieware configured to facilitate identifying a location of an elementvisible in both the first and second infrared images.
 14. The system ofclaim 10, wherein the object is a component of a vehicle.
 15. The systemof claim 14, wherein at least one of the set of infrared devices islocated off of the vehicle.
 16. The system of claim 9, the analyzingincluding examining a portion of each of a plurality of infrared imagesthat includes a component of the object subject to heating during use ofthe component.
 17. The system of claim 16, the examining includingcomparing a brightness of a portion of the component with an expectedbrightness.
 18. A system comprising: a set of infrared imaging devicesconfigured to acquire infrared data of an object during operation of theobject; and a computer system configured to evaluate the object for atleast one heat related anomaly, the computer system including at leastone computing device for performing a method comprising: pre-processinginfrared data corresponding to at least one side of the object, whereinthe pre-processing includes at least one of: applying an imagedeblurring algorithm to at least a portion of the infrared data orfiltering at least a portion of the infrared data; processing thepre-processed infrared data to enhance a set of signal features in thepre-processed infrared data and extract infrared data corresponding toat least a portion of the object from the pre-processed infrared data,wherein the processing uses at least one enhancement process selectedfrom a group of enhancement processes based on the at least one heatrelated anomaly, the group of enhancement processes including:segmentation, fusion of multiple infrared images in the infrared data,edge detection within an infrared image in the infrared data, applyingthresholding to assign a pixel in the infrared data to white or black,and feature detection of the at least a portion of the object in theinfrared data; analyzing the processed infrared data to determine apresence of the at least one heat related anomaly; and providing aresult of the analyzing for use in an evaluation of the object.
 19. Thesystem of claim 18, wherein the object is a component of a vehicle, theat least one anomaly including at least one of: a brake anomaly, abearing anomaly, or a wheel anomaly.
 20. The system of claim 19, whereinat least one of the set of infrared devices is located off of thevehicle.